Macroeconomic data are subject to revisions as later vintages released. Yet, the usual way of generating real-time density forecasts from BVAR models makes no allowance for this form uncertainty. We evaluate two methods that consider uncertainty when forecasting with with/without stochastic volatility. First, model is estimated on vintages. Second, a included, so on, and conditioned estimates r...

This paper motivates and develops a nonlinear extension of the Vector Autoregressive model which we call the Vector Floor and Ceiling model. Bayesian and classical methods for estimation and testing are developed and compared in the context of an application involving U.S. macroeconomic data. In terms of statistical signi ̄cance both classical and Bayesian methods indicate that the (Gaussian) li...

This research included the bayesian estimate for vector Autoregressive model with rank (p) in addition to statistical tests and predict Bayesian when random error of followed generalized multivariate modified Bessel distribution. The prior information about parameters is represented by probability distributions belong conjugate families. It found that posterior marginal distribution matrix (Φ) ...

In this paper we consider nonhomogeneous autoregressive processes which are special cases of the vector-valued autoregressive processes considered by Anderson (1978) for the analysis of panel survey data. We point out that, for a nonhomogeneous autoregressive process of order higher than one, the least-squares estimates cannot be obtained unless repeated measurements are made on the time series...

 We consider the problem of model selection in vector autoregressive model with Normal innovation. Tests such as Vuong's and Cox's tests are provided for order and model selection, i.e. for selecting the order and a suitable subset of regressors, in vector autoregressive model. We propose a test as a modified log-likelihood ratio test for selecting subsets of regressors. The Europe oil prices, ...

In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is empirically fo...